Prognostic Value of the Serum Creatinine-to-Albumin Ratio for Short-term and Long-term Mortality Among Patients with Aortic Aneurysm: A Retrospective Cohort Study Running title: Creatinine-to-albumin ratio predicts mortality in patients with aortic aneurysms

Prognostic Value of the Serum Creatinine-to-Albumin Ratio for Short-term and Long-term Mortality Among Patients with Aortic Aneurysm: A Retrospective Cohort Study Running title: Creatinine-to-albumin ratio predicts mortality in patients with aortic aneurysms | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prognostic Value of the Serum Creatinine-to-Albumin Ratio for Short-term and Long-term Mortality Among Patients with Aortic Aneurysm: A Retrospective Cohort Study Running title: Creatinine-to-albumin ratio predicts mortality in patients with aortic aneurysms Dongqin Cai, Juntao Fang, Jianming Wei, Chao Gong, Xiaojie Chen, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6935801/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Aortic aneurysm is a life-threatening vascular disorder marked by progressive aortic dilation. Early stages are often clinically silent, and diagnosis depends predominantly on incidental imaging, which limits opportunities for timely treatment. Conventional inflammatory and thrombotic biomarkers demonstrate modest specificity and are vulnerable to systemic confounders. The serum creatinine-to-albumin ratio-a composite indicator of inflammation, metabolic stress, and nutritional status-has proven prognostic relevance in other cardiovascular settings but remains unexamined in aortic aneurysm. Methods We conducted a retrospective cohort study using adult patient data from the MIMIC-III and MIMIC-IV intensive care databases. Baseline serum creatinine and albumin measurements defined the ratio, and an optimal cutoff (0.391) was derived by maximally selected rank statistic. Patients were stratified into high- and low-ratio groups. Kaplan-Meier analysis compared 28-day and one-year survival probabilities, while multivariable Cox proportional hazards models quantified the ratio’s association with mortality, adjusting for demographic and clinical covariates. Restricted cubic spline regression assessed nonlinear risk relationships, and sensitivity analyses at 90 and 180 days verified temporal consistency. Subgroup analyses evaluated effect modification by age, aneurysm rupture, and other key factors. Results Among 1,970 patients, a CAR of ≥ 0.391 was associated with significantly worse survival. Multivariate Cox regression revealed higher CAR levels were linked to increased mortality risk: 28-day mortality (hazard ratio (HR) 1.53; 95% confidence interval (95%CI) 1.09–2.14) and 1-year mortality (HR 1.51; 95% CI 1.24–1.85). Kaplan-Meier analysis showed reduced survival rates in high CAR patients at all time points ( P < 0.001). Sensitivity analyses confirmed consistent associations with 90-day and 180-day mortality ( P < 0.001). Restricted cubic spline analysis demonstrated a nonlinear increase in mortality risk with rising CAR values. Subgroup analyses identified older patients and those with ruptured aneurysms as particularly vulnerable. Conclusion The serum creatinine-to-albumin ratio is a simple, low-cost prognostic biomarker in aortic aneurysm. A cutoff of 0.391 reliably identifies individuals at elevated short-term and long-term mortality risk, supporting its use in early risk stratification and personalized management. creatinine-to-albumin ratio aortic aneurysm mortality prognosis rupture Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Aortic aneurysm (AA) is defined by a focal dilation of the aorta exceeding 50% of its normal diameter[1]. Population studies estimate AA prevalence between 2% and 12%, rising to roughly 8% in men over age 65[2], Early disease is typically asymptomatic, yet aneurysmal growth carries a substantial risk of dissection and rupture, with mortality rates up to 90% once rupture occurs[3, 4]. Because AA often progresses silently, most cases are discovered incidentally on computed tomography angiography or ultrasound at advanced stages, precluding timely prophylactic repair[5]. Given the lack of effective pharmacological therapies to arrest aneurysm expansion, robust risk stratification is critical to guide surveillance intervals and surgical referral. A variety of blood-based markers including D-dimer, C-reactive protein, interleukin-6, tumor necrosis factor-α, neutrophil-to-lymphocyte ratio[6–8], and other hematologic indices have been investigated for AA prognostication While readily available and inexpensive, these biomarkers suffer from limited specificity and are easily confounded by coexisting infections, trauma, or systemic inflammation[9, 10]. The serum creatinine-to-albumin ratio (CAR) has recently emerged as an integrative indicator of renal function, metabolic stress, systemic inflammation, and nutritional status. Elevated creatinine reflects renal impairment, muscle wasting, and microvascular dysfunction—all factors implicated in aneurysm progression[11–15]. Meanwhile, albumin serves as a key indicator of nutritional status and systemic inflammation, also plays a pivotal role in AA pathogenesis. Hypoalbuminemia is linked to pro-inflammatory mechanisms, including early myeloid cell infiltration in the aortic wall that will induce vulnerability of aortic, which can accelerate aneurysm development and rupture[16]. Furthermore, low serum albumin at admission is independently associated with increased short- and long-term mortality, prolonged hospitalization, and extended intensive care unit (ICU) stays[17]. These findings highlight the dual importance of creatinine and albumin in both physiological monitoring and risk stratification for AA patients. In other critical illnesses and cardiovascular conditions—such as hemorrhagic stroke, heart failure, acute pancreatitis, and post-cardiac surgery high CAR predicts adverse outcomes[18–21], but its relevance in AA remains unexplored. To bridge this critical knowledge gap, we conducted a retrospective cohort study utilizing the MIMIC-III and MIMIC-IV databases to analyze data from admitted patients with AA between 2001 and 2022. The primary objective was to investigate the association between CAR levels and all-cause mortality in this population, while also determining its relationship with AA-specific mortality and rupture risk, particularly in critically ill patients. Furthermore, we aimed to assess the feasibility of CAR as a simple, cost-effective, and reproducible tool for AA risk stratification. This investigation represents the first systematic exploration of CAR’s role in AA patients, potentially paving the way for improved early detection and individualized management strategies that enhance clinical outcomes through targeted identification of high-risk individuals. Methods Data Sources Retrospective analysis of patients from the MIMIC-III and MIMIC-IV databases. Data extraction was performed by the first author, who successfully passed the Collaborative Institutional Training Initiative examination and was recognized as having such qualifications for this purpose in the database (Record ID 62160837). As this study exclusively used publicly available, anonymized data, ethical review was deemed unnecessary. Study population A total of 546 patients from MIMIC-III and 7,653 patients from MIMIC-IV were initially identified as having aortic aneurysms based on ICD-9 and ICD-10 diagnosis codes. After excluding patients with multiple hospital admissions (MIMIC-III: n = 32; MIMIC-IV: n = 3,914), only first-time hospitalizations were included (MIMIC-III: n = 514; MIMIC-IV: n = 3,739). Patients were further excluded if they met any of the following criteria: (1) age under 18 years; (2) end-stage renal disease or cirrhosis; (3) a hospital stay of less than 24 hours; or (4) missing serum creatinine or albumin values within the first 24 hours of admission. After applying these exclusion criteria, 295 patients from MIMIC-III and 1,675 from MIMIC-IV were included, resulting in a final combined cohort of 1,970 patients. Based on the optimal cutoff value of the CAR, determined using the maximally selected rank statistics, patients were stratified into a low-CAR group (CAR < 0.391, n = 1,369) and a high-CAR group (CAR ≥ 0.391, n = 601). The primary outcomes of this study were 28-day all-cause mortality and 1-year all-cause mortality. The process of patient selection was depicted in Fig. 1 . Variable extraction All data extraction was performed via R (version 4.3.1), with a focus on the baseline variables of AA among individuals. The extracted variables included: the sites of aneurysms, including the abdominal, thoracic, thoracoabdominal and other sites, multiple site aneurysms were defined as multi-site aneurysms; demographic information such as race, gender and age; clinical laboratory tests such as alanine aminotransferase (ALT), albumin (ALB), alkaline phosphatase, aspartate aminotransferase (AST), total bilirubin, chloride, creatinine (CR), potassium, sodium, urea nitrogen, hematocrit, hemoglobin, international normalized ratio (INR), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), mean corpuscular volume (MCV), platelet count, prothrombin time (PT), activated partial thromboplastin time (APTT), red blood cell distribution width (RDW), red blood cells, and white blood cells; and glucose; the recorded medication history including antiplatelets, calcium channel blockers (CCB), and vasoactive drug; comorbidities including acute kidney injury (AKI); chronic obstructive pulmonary disease; atrial fibrillation; coronary heart disease; hypertension; and no choice procedure for treating aortic aneurysms(conversation therapy). The creatinine to albumin ratio was calculated by dividing the serum creatinine level (mg/L) by the serum albumin level (g/L), both measured within the first 24 hours of hospital admission. Statistical analysis Variables with more than 10% missing values were excluded. Variables with missing ratios between 5% and 10% were processed via multiple interpolation to fill in the missing values. Variables with less than 5% missing values were replaced with the mean value, and the abnormal values of variables were addressed via the winsorize method with 1% and 99% as cutoff points. After the Maximally Selected Rank Statistics method determined the ideal CAR cutoff value based on 28-day and 1-year mortality following hospital admission, we divided the patients into low-CAR and high-CAR groups using this cutoff. Restricted cubic spline (RCS) analysis was used to assess whether the association between CAR, treated as a continuous variable, and all-cause mortality was linear or nonlinear. The placement of knots was based on the distribution of CAR in the study population. This approach allows for a more flexible assessment of the exposure–outcome relationship, without assuming a priori thresholds or linear effects. K‒M survival curves were generated to visualize the survival probabilities over time for the high-CAR and low-CAR groups. Log-rank tests were used to compare survival differences between the two groups. Further analysis was conducted via a Cox proportional hazards regression model adjusted for multiple covariates to examine the relationships between CAR levels and mortality rates at different time points. We further constructed a sensitivity analysis about the association of the CAR with mortality at multiple time points at day 90 and day 180, for the model's robustness and temporal stability. All analyses were performed via R version 4.43. Results Baseline Characteristics of Patients with Aortic Aneurysms Among the patients included in this study, 698 (35.4%) were women, and 1272 (64.6%) were men. Among the patients with AA, 601 (30.5%) had a CAR greater than or equal to 0.391, with a 1-year mortality of 44.6%. The other 1369 (69.5%) patients with AA had a CAR value less than 0.391, with a 1-year mortality of 21%. The high-CAR group tended to be older than the low-CAR group (75.01 ± 11.21 vs. 70.48 ± 13.03 years, P < 0.001). Additionally, the high-CAR group presented significantly higher rates of AKI (67.1% vs. 18.3%), hypertension (79.7% vs. 73.1%), and abdominal aneurysm (60.6% vs. 42.8%) than did the low-CAR group. Among the laboratory indicators, patients in the high CAR group had elevated levels of ALT, alkaline phosphatase, AST, bicarbonate, total bilirubin, chloride, creatinine, urea nitrogen, INR, PT, APTT, RDW, WBC, and glucose. Results showed that the high-CAR group has a greater risk of poor prognosis: 28-day mortality (19.6% vs. 6.6%, P < 0.001) and 1-year mortality (44.6% vs. 21%, P < 0.001). The detailed results were listed in (Table 1 in Additional file 1). Table 1 Summary of Baseline characteristics and outcomes of patients with aortic aneurysm Variable Overall High CAR group Low CAR group P value n 1970 601 1369 Age, y 71.86(12.67) 75.01(11.21) 70.48(13.03) < 0.001 Gender, n (%) male 1272(64.6) 429(71.4) 843(61.6) < 0.001 female 698(35.4) 172(28.6) 526(38.4) Race, n (%) white 1470(74.6) 436(72.5) 1034(75.5) 0.179 other 500(25.4) 165(27.5) 335(24.5) Sites of aneurysm, n (%) abdominal, n (%) 876 (44.5) 334(55.6) 542 (39.6) < 0.001 multi-site aneurysms, n (%) 93 (4.7) 34 (5.7) 59 (4.3) others, n (%) 271 (13.8) 104 (17.3) 167 (12.2) thoracic, n (%) 669 (34.0) 112 (18.6) 557 (40.7) thoracoabdominal, n (%) 61 (3.1) 17 (2.8) 44 (3.2) aneurysm rupture, n (%) 107(5.4) 63(10.5) 44(3.2) < 0.001 Comorbidities AKI, n (%) 654(33.2) 403(67.1) 251(18.3) < 0.001 AF, n (%) 773(39.2) 275(45.8) 498(36.4) < 0.001 CHF, n (%) 594(30.2) 240(39.9) 354(25.9) < 0.001 CHD, n (%) 749(38.0) 260(43.3) 489(35.7) 0.002 COPD, n (%) 318(16.1) 111(18.5) 207(15.1) 0.073 HTN, n (%) 1480(75.1) 479(79.7) 1001(73.1) 0.002 Interventions antiplatelet, n (%) 1447(73.5) 412(68.6) 1035(75.6) 0.001 CCB, n (%) 600(30.5) 191(31.8) 409(29.9) 0.428 vasoactive drug, n (%) 1190(60.4) 391(65.1) 799(58.4) 0.006 conservation therapy, n (%) 1455(73.9) 449(74.7) 1006(73.5) 0.607 Laboratory indicators ALT, IU/L 53.55(160.47) 87.15(241.49) 38.80(103.79) < 0.001 Albumin, g/dL 3.41(0.70) 2.95(0.64) 3.61(0.62) < 0.001 Alkaline phosphatase, IU/L 94.36(101.39) 104.90(155.50) 89.73(64.19) 0.002 AST, IU/L 72.45(225.51) 126.67(356.79) 48.65(124.52) < 0.001 Bicarbonate, m Eq/L 24.19(4.32) 22.51(5.02) 24.92(3.74) < 0.001 Total bilirubin, mg/dL 0.89(1.27) 0.99(1.44) 0.85(1.19) 0.018 Chloride, m Eq/L 103.24(5.38) 104.02(6.42) 102.90(4.82) < 0.001 Creatinine, mg/dL 1.23(0.77) 1.92(1.06) 0.92(0.24) < 0.001 Potassium, m Eq/L 4.23(0.66) 4.44(0.75) 4.14(0.58) < 0.001 Sodium, m Eq/L 138.89(4.15) 138.95(4.86) 138.86(3.80) 0.66 Urea nitrogen, mg/dL 24.18(15.47) 36.88(20.01) 18.61(8.16) < 0.001 Hematocrit, % 35.22(6.59) 33.07(6.57) 36.16(6.37) < 0.001 Hemoglobin, g/dL 11.64(2.30) 10.83(2.20) 12.00(2.25) < 0.001 INR, PT 1.39(0.64) 1.54(0.75) 1.33(0.57) < 0.001 MCH, pg 30.17(2.52) 30.04(2.66) 30.23(2.45) 0.118 MCHC, % 33.03(1.59) 32.78(1.68) 33.14(1.54) < 0.001 MCV, fL 91.42(6.53) 91.74(6.96) 91.28(6.33) 0.147 Platelet, K/uL 215.40(99.03) 203.59(103.21) 220.59(96.72) < 0.001 PT, s 15.12(6.24) 16.60(7.48) 14.47(5.48) < 0.001 APTT, s 38.49(25.25) 40.91(27.90) 37.43(23.93) 0.005 RDW, % 14.66(1.99) 15.33(2.01) 14.37(1.91) < 0.001 RBC, m/uL 3.87(0.76) 3.63(0.76) 3.98(0.74) < 0.001 WBC, K/uL 9.94(5.23) 11.42(6.42) 9.30(4.46) < 0.001 Glucose, mg/dL 127.69(52.26) 141.30(62.94) 121.71(45.57) < 0.001 Mortality, n (%) 28-day mortality 209(10.6) 118(19.6) 91(6.6) < 0.001 1-year mortality 556(28.2) 268(44.6) 288(21.0) < 0.001 Abbreviations : AF, atrial fibrillation; AKI, acute kidney injury; ALT, alanine aminotransferase; AST, aspartate aminotransferase; CCB, calcium channel blockers CHF, congestive heart failure; CHD, coronary heart disease; COPD, chronic obstructive pulmonary disease; HTN, hypertension; MBP, mean blood pressure; INR, international normalized ratio; MCH, mean corpuscular hemoglobin; MCHC, mean corpuscular hemoglobin concentration; MCV, mean corpuscular volume; PT, prothrombin time; APTT, activated partial thromboplastin time; RBC: red blood cells; RDW, red blood cell distribution width; WBC: white blood cells. Survival Analysis According to the CAR KM analysis revealed significantly higher mortality rates in the high-CAR group than in the low-CAR group across all time points (log-rank P < 0.001 for all). Specifically, the 28-day mortality rate was 19.6% in the high CAR group versus 6.6% in the low CAR group (Fig. 2 A), and the 1-year mortality rates were 44.6% and 21.0%, respectively (Fig. 2 B). The results revealed that patients with high CAR characteristics had poorer prognoses than those with low CAR characteristics, both in terms of short-term and long-term survival. Associations between the CAR and mortality risk Cox proportional hazards models demonstrated that the CAR was independently associated with both short- and long-term mortality after adjusting for confounders. When CAR was used as a continuous variable, for 28-day mortality, the hazard ratio (HR) and 95% confidence interval (CI) were reported as 2.454 (2.04–2.95), 2.679 (2.17–3.3), and 1.873 (1.39–2.53) in Model 0 (unadjusted), Model 1 (adjusted for age, gender, and race), and Model 2 (further adjusted for age, gender, race, white blood cell, red blood cell distribution width, vasoactive drug, conservation therapy, total bilirubin, aneurysm rupture, AKI, congestive heart failure, coronary heart disease, chronic obstructive pulmonary, atrial fibrillation, conservation therapy, hypertension and glucose), respectively (all P < 0.05), and for 360-day mortality, the HR and 95% CI were 2.21 (1.94–2.51) in Model 0, 2.388 (2.06–2.8) in Model 1, and 1.955 (1.61–2.37) in Model 2 (all P < 0.0001). When CAR was used as a binary variable, the results revealed that the 28-day mortality for HR and 95% CI were 3.19 (2.40–4.20), 2.816 (2.13–3.7), and 1.668 (1.22–2.29), respectively (all P < 0.05), and the 1-year mortality were 2.21 (1.94–2.51), 2.388 (2.06–2.8), and 1.955 (1.61–2.37), respectively (all P < 0.0001) in Model 0, Model 1 and Model 2 (Table 2 ). Table 2 Univariate and multivariate Cox regression analyses of 28day and 1year all-cause mortality association with CAR in patients with AA. Variable Model 0 Model 1 Model 2 28day all-cause mortality HR(95%CI) P-value HR(95%CI) P-value HR(95%CI) P-value CAR ≥ 0.391 a 3.19 (2.40–4.20) < 0.001 2.82 (2.13–3.70) < 0.001 1.40 (1.01–1.90) 0.043 CAR b 2.45 (2.04–2.95) < 0.001 2.68 (2.17–3.31) < 0.001 1.53 (1.09–2.14) 0.013 1year all-cause mortality HR(95%CI) P-value HR(95%CI) P-value HR(95%CI) P-value CAR ≥ 0.391 a 2.58 (2.20–3.05) < 0.001 2.23 (1.88–2.60) < 0.001 1.52 (1.24–1.85) < 0.001 CAR b 2.21 (1.94–2.51) < 0.001 2.39 (2.06–2.77) < 0.001 1.72 (1.39–2.12) < 0.001 Model 0: unadjusted; Model 1: adjusted for age, gender, and race; Model 2: adjusted for Age, gender, race, white blood cell, red blood cell distribution width, vasoactive drug, conservation therapy, total bilirubin, aneurysm rupture, AKI, congestive heart failure, coronary heart disease, chronic obstructive pulmonary, atrial fibrillation, conservation therapy, hypertension and glucose. a , CAR as a binary variable; b , CAR as a continuous variable. Sensitivity analysis of CAR on mortality association over multiple time points To further evaluate the robustness of the association between the CAR and patient mortality, sensitivity analyses were conducted using additional clinical endpoints. Cox proportional hazards regression models were applied to assess the association of CAR across 3-month and 6-month all-cause mortality. The same covariates used in the primary multivariate models were included in the adjustment to ensure consistency across time points. In the sensitivity analyses, When CAR was used as a continuous variable, CAR remained significantly associated with increased risk of mortality at all alternative time points, HR(95%CI)of 3-month mortality 1.64 (1.27–2.11), and 6-month mortality 1.67 (1.32–2.10), when CAR was used as a binary variable, higher CAR remained significantly associated with increased risk of mortality at 90-day 1.60 (1.25–2.05) and 180 day 1.59 (1.27–1.99). The trend was consistent with the results observed in the 28-day and 1-year mortality models, supporting the robustness and temporal stability of the association between CAR and adverse outcomes in patients with aortic aneurysms. Further details of these sensitivity analyses, including model coefficients and confidence intervals, are provided in (Table 3 ). Table 3 Univariate and multivariate Cox regression analyses of 90-day and 180-day all-cause mortality association with CAR of AA patients Variable Model 0 Model 1 Model 2 180day all-cause mortality HR(95%CI) P-value HR(95%CI) P-value HR(95%CI) P-value CAR ≥ 0.391 a 3.00 (2.50–3.80) < 0.001 2.69 (2.17–3.30) < 0.001 1.60 (1.24–2.05) < 0.001 CAR b 2.33 (2.00–2.70) < 0.001 2.51 (2.11–2.98) < 0.001 1.64 (1.27–2.11) < 0.001 180day all-cause mortality HR(95%CI) P-value HR(95%CI) P-value HR(95%CI) P-value CAR ≥ 0.391 a 2.91 (2.40– 3.50) < 0.001 2.55 (2.10–3.10) < 0.001 1.59 (1.27–1.99) < 0.001 CAR b 2.28 (1.98–2.62) < 0.001 2.47 (2.10–2.90) < 0.001 1.67 (1.32–2.10) < 0.001 Model 0: unadjusted; Model 1: adjusted for age, gender, and race; Model 2: adjusted for Age, gender, race, white blood cell, red blood cell distribution width, vasoactive drug, conservation therapy, total bilirubin, aneurysm rupture, AKI, congestive heart failure, coronary heart disease, chronic obstructive pulmonary, atrial fibrillation, conservation therapy, hypertension and glucose. a , CAR as a binary variable; b , CAR as a continuous variable. Evaluation of the nonlinear association between the CAR and all-cause mortality We used RCS analysis to assess whether a linear relationship exists between CAR and 28-day and 1-year all-cause mortality. The adjusted RCS curve revealed a significant association between the CAR and short- and long-term all-cause mortality ( P for overall < 0.001), whereas a significant nonlinear effect was observed ( P for nonlinearity < 0.001). This suggested that the CAR was associated with 28-day and 1-year mortality, with a key threshold of 0.391, above which the risk of mortality increased. For short-term mortality, high CAR levels were significantly associated with an increased risk of mortality (HR = 3.0, 95% CI: 2–4.19, P < 0.001), whereas low CAR levels were significantly associated with a decreased risk of mortality (HR = 0.3, 95% CI: 0.2–0.412, P < 0.001) (Fig. 3 A). Similarly, with respect to long-term mortality, comparable results were observed (Fig. 3 B). The results revealed the nonlinear relationship. Exploratory subgroup analysis of the CAR and mortality To investigate the association between CAR and all-cause mortality in patients with AA, exploratory subgroup analyses were performed by stratifying the population according to gender, race, age ≥ 65 years, aneurysm rupture status, and aneurysm sites. Overall, elevated CAR was significantly associated with increased risk of both 28-day and 1-year all-cause mortality across nearly all subgroups. For 28-day mortality, the (HR -2.45, 95% CI: 2.04–2.95; P < 0.001; Fig. 4 A). Similarly, for 1-year mortality, the (HR 2.21, 95% CI: 1.94–2.51; P < 0.001; Fig. 4 B). At 28 days, A significant interaction was observed in the analysis of 28-day mortality (P for interaction = 0.034). The association between elevated CAR and short-term mortality was stronger among other races (HR: 3.36, 95% CI: 2.42–4.66) compared to White patients (HR: 2.18, 95% CI: 1.68–2.82). Additionally, a significant interaction was also noted for 1-year mortality (P for interaction < 0.05), indicating that the association between elevated CAR and long-term mortality was more pronounced in other race patients (HR: 2.03, 95% CI: 2.73–4.07) compared to White patients (HR: 3.10, 95% CI: 1.37–2.38). Significant interactions were observed for race, age ≥ 65 years, and aneurysm rupture in the association between CAR and 1-year all-cause mortality (all P for interaction < 0.05). Notably, the interaction effects of age and aneurysm rupture on CAR were only evident in 1-year mortality, but not in short-term outcomes. Among patients aged < 65 years, elevated CAR was associated with an increased risk of death (HR = 2.10, 95% CI: 1.63–2.72, P < 0.001), while the association was even stronger in those aged ≥ 65 years (HR = 3.00, 95% CI: 2.43–3.69, P < 0.001; P for interaction = 0.036). Similarly, the effect of CAR was more pronounced in patients with ruptured aneurysms (HR = 2.98, 95% CI: 1.75–5.16, P < 0.001) compared to those without rupture (HR = 2.14, 95% CI: 1.86–2.46, P < 0.001; P for interaction = 0.031). Discussion In this study, we found that elevated CAR levels were significantly associated with increased all-cause mortality in patients with AA, both in the short term (28-day mortality) and long term (1-year mortality). Notably, both short-term and long-term mortality rates were significantly higher among patients with CAR ≥ 0.391 compared to those with CAR < 0.391, highlighting the prognostic value of CAR in this patient population. Restricted cubic spline analysis suggests a potentially nonlinear association, suggesting that even modest increases in CAR may herald disproportionately higher risk beyond the inflection point, highlighting the complexity of the role of CAR in predicting the mortality of AA patients. This difference may be attributed to variations in patient demographics and the inclusion of diverse aneurysm types in our cohort. Mechanistically, elevated creatinine reflects not only impaired renal clearance but also muscle wasting and microvascular dysfunction—processes that contribute to aneurysm growth and weakening of the aortic wall. Hypoalbuminemia, on the other hand, indicates poor nutritional reserves and heightened inflammatory activity, both of which accelerate extracellular matrix degradation in the aortic wall. The creatinine-to-albumin ratio (CAR) integrates these pathophysiological pathways, providing a more comprehensive assessment of patient vulnerability than either marker alone. Our findings align with previous studies that have demonstrated the prognostic utility of CAR across various clinical conditions, including acute kidney injury (AKI) and cardiovascular diseases[22]. For instance, CAR has been identified as an independent predictor of mortality in patients with chronic heart failure, reflecting the interplay between renal dysfunction, systemic inflammation, and nutritional depletion key determinants of prognosis in this population. Similarly, in patients with acute coronary syndrome (ACS) undergoing percutaneous coronary intervention (PCI), CAR has been shown to outperform individual biomarkers such as creatinine or albumin in risk stratification, highlighting its value as a composite marker that integrates multiple pathophysiological pathways[23, 24]. Our study extends these findings by demonstrating that CAR is a robust predictor of mortality in patients with aortic aneurysm (AA), underscoring its broader applicability in vascular disease contexts. The strong association between CAR and mortality in AA patients may reflect underlying renal function, nutritional status and inflammatory. Well established markers of renal function, such as elevated creatinine, may cause the poor clinical outcomes such as heart failure[25], acute coronary syndrome[26], have been identified, and high creatinine could be highly dangerous factor; for example, aneurysm rupture is variable in predicting the prognosis of AA patients, and higher rates of postoperative morbidity and mortality[27]. This aligns with prior evidence showing that impaired renal function, manifested by elevated creatinine, contributes to systemic inflammation and metabolic disturbances, which are critical drivers of adverse outcomes in vascular diseases. The most abundant protein in human plasma and primarily synthesized by hepatocytes, plays a critical role in maintaining colloid osmotic pressure, transporting various endogenous and exogenous substances, and modulating inflammatory and oxidative stress responses. Low serum albumin levels are widely recognized as a marker of systemic illness severity and poor nutritional status, and have been consistently associated with adverse clinical outcomes across a broad spectrum of diseases[28–36], including cardiovascular diseases, respiratory diseases, digestive diseases, tumor diseases, urinary diseases and so on, some of those diseases may to be the acceleration of development about the AA. In particular, hypoalbuminemia has been implicated in the pathogenesis of abdominal aortic aneurysm (AAA), potentially through mechanisms involving impaired antioxidant defense and increased vascular wall inflammation. Experimental and clinical evidence suggests that albumin exerts protective effects by promoting the synthesis of anti-inflammatory mediators, such as lipoxins and other bioactive molecules during periods of oxidative stress, which may help mitigate vascular damage and reduce AAA expansion risk [37, 38]. Moreover low preoperative serum albumin levels have been independently associated with increased postoperative morbidity and mortality following endovascular aneurysm repair (EVAR)[39]. This association underscores the importance of nutritional status in surgical outcomes and supports the potential value of preoperative nutritional optimization. Indeed, studies have demonstrated that albumin supplementation or targeted nutritional support can improve clinical outcomes in patients with severe hypoalbuminemia and poor baseline health status[40]. Clinically, the CAR offers a highly convenient, rapid, and economical means of risk stratification in aortic aneurysm care. Because it relies solely on serum creatinine and albumin—tests already performed on virtually every patient at admission CAR incurs no additional cost or workload, can be calculated at the bedside in seconds, and is immediately actionable. Its universal availability makes it suitable for high-tech centers and resource-limited settings alike. By pinpointing high-risk individuals early, CAR facilitates targeted surveillance, timely intervention, and more efficient allocation of healthcare resources, thereby enhancing patient safety and optimizing treatment outcomes. Our analysis revealed a consistent and statistically significant association between elevated CAR levels and increased all-cause mortality across multiple time points, ranging from the acute phase (28-day mortality) to longer-term follow-up (1-year mortality). Notably, sensitivity analyses further validated this association at intermediate time intervals, reinforcing the stability and reliability of CAR as a prognostic marker throughout the clinical course of AA. Additionally, subgroup analyses also highlighted variations in the predictive performance of CAR across different patient populations. For instance, the prognostic value of CAR was particularly pronounced among male patients and older adults (> 65 years), those with aortic aneurysm[41], suggesting that age and sex may modulate the relationship between functional status of renal, nutritional status, and mortality in AA. These findings align with existing literature indicating that older individuals and males are at higher risk for adverse outcomes following vascular events. Furthermore, we observed that certain racial subgroups, such as other populations including Black and Asian patients, tended to present with larger aneurysm diameters[42]. This finding may reflect disparities in disease detection, access to care, or underlying biological differences, which could contribute to greater aneurysm complexity and increased technical challenges during interventions such as EVAR. The interplay between race, aneurysm morphology, and CAR levels warrants further investigation to better understand how these factors influence clinical decision-making and outcomes. Therefore, these insights support the application of CAR within a personalized medicine framework, where individual patient characteristics such as age, gender, race, and baseline comorbidities can inform tailored risk assessment and management strategies. Importantly, the predictive power of CAR spans both the immediate post-diagnosis period and long-term follow-up, offering clinicians a unified and dynamic biomarker for continuous risk monitoring throughout the disease trajectory of aortic aneurysm. There are several limitations that warrant consideration. Firstly, the retrospective nature of our analysis may introduce inherent biases, including missing data and unmeasured confounding variables. Additionally, certain important clinical parameters, such as aneurysm diameter, were not available and could not be adjusted for in the analysis. Secondly, although our cohort included patients from multiple racial backgrounds, the majority were White, and all data were derived from a single center in the United States. These factors may limit the generalizability of our findings. Therefore, future multicenter studies with more ethnically and geographically diverse populations are needed to externally validate our results. Finally, although CAR reflects the interplay between renal function and nutritional status, it remains unclear whether therapeutic strategies targeting these underlying components can influence CAR levels or improve outcomes in AA patients. Future mechanistic and interventional studies are warranted to explore this potential. Conclusion In patients with aortic aneurysm, the serum creatinine-to-albumin ratio independently predicts both short-term and long-term all-cause mortality. The CAR cutoff of 0.391 provides a simple, low-cost, and widely accessible metric for early identification of high-risk patients, supporting personalized surveillance and treatment strategies that can improve clinical care across diverse practice settings. Abbreviations AA Aortic aneurysm AAA Abdominal aortic aneurysm ACS Acute coronary syndrome AF Atrial fibrillation AKI Acute kidney injury ALT Alanine aminotransferase AST Aspartate aminotransferase APTT Activated partial thromboplastin time CAR Creatinine-to-albumin ratio CCB Calcium channel blockers CHF Congestive heart failure CHF Congestive heart failure CHD Coronary heart disease COPD Chronic obstructive pulmonary disease CTA Computed Tomography Angiography EVAR Endovascular aneurysm repair HR Hazard ratio INR International normalized ratio ICU Intensive care unit (ICU) KM Kaplan-Meier survival analysis MCH Mean corpuscular hemoglobin MCHC Mean corpuscular hemoglobin concentration MCV Mean corpuscular volume MIMIC Medical Information Mart for Intensive Care NLR N eutrophil-to-lymphocyte ratio RCS Restricted cubic spline RDW Red blood cell distribution width RBC Red blood cells PCI Percutaneous coronary intervention PT Prothrombin time WBC White blood cells 95% CI 95% confidence interval Declarations Author contributions The following authors contributed to the preparation of the manuscript as follows: DC: Data gathering, Study design, Statistical analysis, manuscript editing, interpretation of data, literature search. JF, JW: Statistical analysis, manuscript editing, interpretation of data, CG, CX, ZW, MY: Data gathering, Study design. AH, GL, LS: Study design. All authors read and approved the final manuscript. Funding Financial backing for the research, writing, and publication of this article was recognized by the author(s), include National Natural Science Foundation of China [Grant no. 82200519 and 8220020445], and the Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China, [Grant no. Y0120220151], Outstanding Young Scientists of Tongji Hospital, Tongji University [Grant no. HBRC 1801] and the Tongji Hospital Internal Training Program [Grant no. ITJ-QN2404], National Natural Science Foundation of China [Grant Numbers: 82400345], 2025 Basic and Applied Basic Research Special Topic: Young Doctoral “Starting Sail” Project [2025A04J4716]. Ethical approval of studies and informed consent The use of public databases does not require ethical approval or informed consent. Conflict of interest statement The authors declare that they have no conflicts of interest. Acknowledgments We would like to extend our sincere gratitude to MIMIC-III and MIMIC-IV participants and staff for their invaluable support and assistance. We would like to thank the funders and all original authors who provided publicly available data. ChatGPT (OpenAI, San Francisco, USA) was used to improve the grammar and clarity of the manuscript. Availability of data and materials The corresponding author can be contacted to receive the datasets generated and utilized in this work upon reasonable request and with MIMIC’s permission. 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Serum Albumin to Creatinine Ratio and Short-Term Clinical Outcomes in Patients With ST-Elevation Myocardial Infarction. Angiology. 2022;73(9):809–17. Johnson MR, Sander JW. The clinical impact of epilepsy genetics. J Neurol Neurosurg Psychiatry. 2001;70(4):428–30. Bansal N, et al. Burden and Outcomes of Heart Failure Hospitalizations in Adults With Chronic Kidney Disease. J Am Coll Cardiol. 2019;73(21):2691–700. Facila L, et al. [Prognostic value of serum creatinine in non-ST-elevation acute coronary syndrome]. Rev Esp Cardiol. 2006;59(3):209–16. Ambler GK, et al. Incidence and Outcomes of Severe Renal Impairment Following Ruptured Abdominal Aortic Aneurysm Repair. Eur J Vasc Endovasc Surg. 2015;50(4):443–9. Swaim MW, Wilson JA. GI emergencies: rapid therapeutic responses for older patients. Geriatrics, 1999. 54(6): pp. 20 – 2, 25 – 6, 29–30 passim. Hiraoka A, et al. Modified predictive score based on frailty for mid-term outcomes in open total aortic arch surgery. Eur J Cardiothorac Surg. 2018;54(1):42–7. Manolis AA, et al. Low serum albumin: A neglected predictor in patients with cardiovascular disease. Eur J Intern Med. 2022;102:24–39. Jiang J, Miao P, Xin G. Prognostic value of albumin-based indices for mortality after heart failure: a systematic review and meta-analysis. BMC Cardiovasc Disord. 2024;24(1):570. Park JK, et al. Impact of Serum Albumin Levels and Body Mass Index on Outcomes of Open Abdominal Aortic Aneurysm Repair in Korean Population. Ann Vasc Surg. 2024;101:139–47. Filloux B, et al. Short-term and long-term vital outcomes of cirrhotic patients admitted to an intensive care unit. Eur J Gastroenterol Hepatol. 2010;22(12):1474–80. Aronen M, et al. The long-term prognostic value of serum 25(OH)D, albumin, and LL-37 levels in acute respiratory diseases among older adults. BMC Geriatr. 2022;22(1):146. Wu N, et al. Low pretherapeutic serum albumin as a risk factor for poor outcome in esophageal squamous cell carcinomas. Nutr Cancer. 2015;67(3):481–5. Philip F, et al. The impact of renal artery stenosis on outcomes after open-heart surgery. J Am Coll Cardiol. 2014;63(4):310–6. Lu Y, et al. Association between lactate/albumin ratio and all-cause mortality in patients with acute respiratory failure: A retrospective analysis. PLoS ONE. 2021;16(8):e0255744. Kaluza J, et al. Anti-inflammatory diet and risk of abdominal aortic aneurysm in two Swedish cohorts. Heart. 2019;105(24):1876–83. Nam WS, et al. Prognostic Value of Serum Albumin in Aortic Aneurysm Patients Undergoing Graft Replacement of Ascending Aorta and Aortic Arch. Int J Med Sci. 2023;20(5):663–8. Bjorck M. Management of the tense abdomen or difficult abdominal closure after operation for ruptured abdominal aortic aneurysms. Semin Vasc Surg. 2012;25(1):35–8. Obel LM, et al. Population-Based Risk Factors for Ascending, Arch, Descending, and Abdominal Aortic Dilations for 60-74-Year-Old Individuals. J Am Coll Cardiol. 2021;78(3):201–11. de Guerre L, et al. Racial Differences in Isolated Aortic, Concomitant Aortoiliac, and Isolated Iliac Aneurysms: This is a Retrospective Observational Study. Ann Surg. 2023;277(1):165–72. Additional. file 1. Additional Declarations No competing interests reported. Supplementary Files Supplementtable.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6935801","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":501686984,"identity":"8c174101-08b5-4db0-a0fc-d39eacc0f62a","order_by":0,"name":"Dongqin Cai","email":"","orcid":"","institution":"South China University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Dongqin","middleName":"","lastName":"Cai","suffix":""},{"id":501686985,"identity":"f5f1de6d-2db5-4e48-835a-07ace9b06df8","order_by":1,"name":"Juntao Fang","email":"","orcid":"","institution":"Guangdong Cardiovascular Institute, Guangdong Provincial 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06:23:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6935801/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6935801/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89396155,"identity":"d38c8cea-ac9b-4d93-98b2-0807ee458544","added_by":"auto","created_at":"2025-08-19 13:39:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":397775,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart illustrating the patient screening process for this study.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6935801/v1/906771932208ae5dca779b1f.png"},{"id":89396157,"identity":"5c333ef9-ab77-4c14-8cd5-b6864bcdec58","added_by":"auto","created_at":"2025-08-19 13:39:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":771333,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier survival analysis of all-cause mortality in patients with AA. (A) 28-day all-cause mortality. (B) 1-year all-cause mortality.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6935801/v1/ec1d3a6302939908db52153f.png"},{"id":89397441,"identity":"73a813c4-1236-4493-9484-ff4319f36d4f","added_by":"auto","created_at":"2025-08-19 13:47:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":694086,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between the CAR and mortality at different time points according to the RCS model. (A). RCS plot depicting the relationship between the CAR and 28-day mortality. (B). RCS plot depicting the relationship between the CAR and 1-year mortality.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6935801/v1/7672af271e55e2127c6e2e63.png"},{"id":89396159,"identity":"c8cf7339-16b7-4ee4-bc1c-901e8fbfe2ae","added_by":"auto","created_at":"2025-08-19 13:39:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2260160,"visible":true,"origin":"","legend":"\u003cp\u003eForest plots of Cox regression analysis of the relationship between all-cause mortality and the CAR in patients with AA across different subgroups. (A) Cox regression forest plots of CAR and 28-day mortality in different subgroups of patients with AA. (B) Cox regression forest plots of CAR and 1-year mortality in different subgroups of patients with AA.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-6935801/v1/204ed614745c0bd8886b0f41.png"},{"id":104401373,"identity":"0210dcb2-02d2-49ad-955a-d7dc9c16efe9","added_by":"auto","created_at":"2026-03-11 12:12:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5053678,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6935801/v1/f7606c28-d7d6-43cb-99de-aa6ea7599cfd.pdf"},{"id":89397440,"identity":"f7767592-36f1-41b3-9e2c-e118ba8509f4","added_by":"auto","created_at":"2025-08-19 13:47:42","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":13225,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementtable.docx","url":"https://assets-eu.researchsquare.com/files/rs-6935801/v1/4404d1d53f5579cbcbf905b8.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prognostic Value of the Serum Creatinine-to-Albumin Ratio for Short-term and Long-term Mortality Among Patients with Aortic Aneurysm: A Retrospective Cohort Study Running title: Creatinine-to-albumin ratio predicts mortality in patients with aortic aneurysms","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAortic aneurysm (AA) is defined by a focal dilation of the aorta exceeding 50% of its normal diameter[1]. Population studies estimate AA prevalence between 2% and 12%, rising to roughly 8% in men over age 65[2], Early disease is typically asymptomatic, yet aneurysmal growth carries a substantial risk of dissection and rupture, with mortality rates up to 90% once rupture occurs[3, 4]. Because AA often progresses silently, most cases are discovered incidentally on computed tomography angiography or ultrasound at advanced stages, precluding timely prophylactic repair[5].\u003c/p\u003e\n\u003cp\u003eGiven the lack of effective pharmacological therapies to arrest aneurysm expansion, robust risk stratification is critical to guide surveillance intervals and surgical referral. A variety of blood-based markers including D-dimer, C-reactive protein, interleukin-6, tumor necrosis factor-\u0026alpha;, neutrophil-to-lymphocyte ratio[6\u0026ndash;8], and other hematologic indices have been investigated for AA prognostication While readily available and inexpensive, these biomarkers suffer from limited specificity and are easily confounded by coexisting infections, trauma, or systemic inflammation[9, 10].\u003c/p\u003e\n\u003cp\u003eThe serum creatinine-to-albumin ratio (CAR) has recently emerged as an integrative indicator of renal function, metabolic stress, systemic inflammation, and nutritional status. Elevated creatinine reflects renal impairment, muscle wasting, and microvascular dysfunction\u0026mdash;all factors implicated in aneurysm progression[11\u0026ndash;15]. Meanwhile, albumin serves as a key indicator of nutritional status and systemic inflammation, also plays a pivotal role in AA pathogenesis. Hypoalbuminemia is linked to pro-inflammatory mechanisms, including early myeloid cell infiltration in the aortic wall that will induce vulnerability of aortic, which can accelerate aneurysm development and rupture[16]. Furthermore, low serum albumin at admission is independently associated with increased short- and long-term mortality, prolonged hospitalization, and extended intensive care unit (ICU) stays[17]. These findings highlight the dual importance of creatinine and albumin in both physiological monitoring and risk stratification for AA patients. In other critical illnesses and cardiovascular conditions\u0026mdash;such as hemorrhagic stroke, heart failure, acute pancreatitis, and post-cardiac surgery high CAR predicts adverse outcomes[18\u0026ndash;21], but its relevance in AA remains unexplored.\u003c/p\u003e\n\u003cp\u003eTo bridge this critical knowledge gap, we conducted a retrospective cohort study utilizing the MIMIC-III and MIMIC-IV databases to analyze data from admitted patients with AA between 2001 and 2022. The primary objective was to investigate the association between CAR levels and all-cause mortality in this population, while also determining its relationship with AA-specific mortality and rupture risk, particularly in critically ill patients. Furthermore, we aimed to assess the feasibility of CAR as a simple, cost-effective, and reproducible tool for AA risk stratification. This investigation represents the first systematic exploration of CAR\u0026rsquo;s role in AA patients, potentially paving the way for improved early detection and individualized management strategies that enhance clinical outcomes through targeted identification of high-risk individuals.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eData Sources\u003c/h2\u003e\u003cp\u003eRetrospective analysis of patients from the MIMIC-III and MIMIC-IV databases. Data extraction was performed by the first author, who successfully passed the Collaborative Institutional Training Initiative examination and was recognized as having such qualifications for this purpose in the database (Record ID 62160837). As this study exclusively used publicly available, anonymized data, ethical review was deemed unnecessary.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eStudy population\u003c/h3\u003e\n\u003cp\u003eA total of 546 patients from MIMIC-III and 7,653 patients from MIMIC-IV were initially identified as having aortic aneurysms based on ICD-9 and ICD-10 diagnosis codes. After excluding patients with multiple hospital admissions (MIMIC-III: n\u0026thinsp;=\u0026thinsp;32; MIMIC-IV: n\u0026thinsp;=\u0026thinsp;3,914), only first-time hospitalizations were included (MIMIC-III: n\u0026thinsp;=\u0026thinsp;514; MIMIC-IV: n\u0026thinsp;=\u0026thinsp;3,739). Patients were further excluded if they met any of the following criteria: (1) age under 18 years; (2) end-stage renal disease or cirrhosis; (3) a hospital stay of less than 24 hours; or (4) missing serum creatinine or albumin values within the first 24 hours of admission. After applying these exclusion criteria, 295 patients from MIMIC-III and 1,675 from MIMIC-IV were included, resulting in a final combined cohort of 1,970 patients.\u003c/p\u003e\u003cp\u003eBased on the optimal cutoff value of the CAR, determined using the maximally selected rank statistics, patients were stratified into a low-CAR group (CAR\u0026thinsp;\u0026lt;\u0026thinsp;0.391, n\u0026thinsp;=\u0026thinsp;1,369) and a high-CAR group (CAR\u0026thinsp;\u0026ge;\u0026thinsp;0.391, n\u0026thinsp;=\u0026thinsp;601). The primary outcomes of this study were 28-day all-cause mortality and 1-year all-cause mortality. The process of patient selection was depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eVariable extraction\u003c/h3\u003e\n\u003cp\u003eAll data extraction was performed via R (version 4.3.1), with a focus on the baseline variables of AA among individuals. The extracted variables included: the sites of aneurysms, including the abdominal, thoracic, thoracoabdominal and other sites, multiple site aneurysms were defined as multi-site aneurysms; demographic information such as race, gender and age; clinical laboratory tests such as alanine aminotransferase (ALT), albumin (ALB), alkaline phosphatase, aspartate aminotransferase (AST), total bilirubin, chloride, creatinine (CR), potassium, sodium, urea nitrogen, hematocrit, hemoglobin, international normalized ratio (INR), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), mean corpuscular volume (MCV), platelet count, prothrombin time (PT), activated partial thromboplastin time (APTT), red blood cell distribution width (RDW), red blood cells, and white blood cells; and glucose; the recorded medication history including antiplatelets, calcium channel blockers (CCB), and vasoactive drug; comorbidities including acute kidney injury (AKI); chronic obstructive pulmonary disease; atrial fibrillation; coronary heart disease; hypertension; and no choice procedure for treating aortic aneurysms(conversation therapy). The creatinine to albumin ratio was calculated by dividing the serum creatinine level (mg/L) by the serum albumin level (g/L), both measured within the first 24 hours of hospital admission.\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eVariables with more than 10% missing values were excluded. Variables with missing ratios between 5% and 10% were processed via multiple interpolation to fill in the missing values. Variables with less than 5% missing values were replaced with the mean value, and the abnormal values of variables were addressed via the winsorize method with 1% and 99% as cutoff points. After the Maximally Selected Rank Statistics method determined the ideal CAR cutoff value based on 28-day and 1-year mortality following hospital admission, we divided the patients into low-CAR and high-CAR groups using this cutoff. Restricted cubic spline (RCS) analysis was used to assess whether the association between CAR, treated as a continuous variable, and all-cause mortality was linear or nonlinear. The placement of knots was based on the distribution of CAR in the study population. This approach allows for a more flexible assessment of the exposure\u0026ndash;outcome relationship, without assuming a priori thresholds or linear effects. K‒M survival curves were generated to visualize the survival probabilities over time for the high-CAR and low-CAR groups. Log-rank tests were used to compare survival differences between the two groups. Further analysis was conducted via a Cox proportional hazards regression model adjusted for multiple covariates to examine the relationships between CAR levels and mortality rates at different time points. We further constructed a sensitivity analysis about the association of the CAR with mortality at multiple time points at day 90 and day 180, for the model's robustness and temporal stability. All analyses were performed via R version 4.43.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eBaseline Characteristics of Patients with Aortic Aneurysms\u003c/h2\u003e\u003cp\u003eAmong the patients included in this study, 698 (35.4%) were women, and 1272 (64.6%) were men. Among the patients with AA, 601 (30.5%) had a CAR greater than or equal to 0.391, with a 1-year mortality of 44.6%. The other 1369 (69.5%) patients with AA had a CAR value less than 0.391, with a 1-year mortality of 21%. The high-CAR group tended to be older than the low-CAR group (75.01\u0026thinsp;\u0026plusmn;\u0026thinsp;11.21 vs. 70.48\u0026thinsp;\u0026plusmn;\u0026thinsp;13.03 years, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Additionally, the high-CAR group presented significantly higher rates of AKI (67.1% vs. 18.3%), hypertension (79.7% vs. 73.1%), and abdominal aneurysm (60.6% vs. 42.8%) than did the low-CAR group. Among the laboratory indicators, patients in the high CAR group had elevated levels of ALT, alkaline phosphatase, AST, bicarbonate, total bilirubin, chloride, creatinine, urea nitrogen, INR, PT, APTT, RDW, WBC, and glucose. Results showed that the high-CAR group has a greater risk of poor prognosis: 28-day mortality (19.6% vs. 6.6%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and 1-year mortality (44.6% vs. 21%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The detailed results were listed in (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e in Additional file 1).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary of Baseline characteristics and outcomes of patients with aortic aneurysm\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh CAR group\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLow CAR group\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1970\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e601\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1369\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, y\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e71.86(12.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e75.01(11.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e70.48(13.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGender, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1272(64.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e429(71.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e843(61.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003efemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e698(35.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e172(28.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e526(38.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRace, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ewhite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1470(74.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e436(72.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1034(75.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.179\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eother\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e500(25.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e165(27.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e335(24.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSites of aneurysm, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eabdominal, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e876 (44.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e334(55.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e542 (39.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emulti-site aneurysms, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e93 (4.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e34 (5.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e59 (4.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eothers, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e271 (13.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e104 (17.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e167 (12.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ethoracic, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e669 (34.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e112 (18.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e557 (40.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ethoracoabdominal, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e61 (3.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17 (2.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e44 (3.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eaneurysm rupture, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e107(5.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e63(10.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e44(3.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eComorbidities\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAKI, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e654(33.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e403(67.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e251(18.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAF, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e773(39.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e275(45.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e498(36.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCHF, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e594(30.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e240(39.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e354(25.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCHD, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e749(38.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e260(43.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e489(35.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCOPD, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e318(16.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e111(18.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e207(15.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.073\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHTN, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1480(75.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e479(79.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1001(73.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eInterventions\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eantiplatelet, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1447(73.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e412(68.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1035(75.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCCB, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e600(30.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e191(31.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e409(29.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.428\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003evasoactive drug, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1190(60.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e391(65.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e799(58.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003econservation therapy, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1455(73.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e449(74.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1006(73.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.607\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLaboratory indicators\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALT, IU/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e53.55(160.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e87.15(241.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e38.80(103.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlbumin, g/dL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.41(0.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.95(0.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.61(0.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlkaline phosphatase, IU/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e94.36(101.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e104.90(155.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e89.73(64.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAST, IU/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e72.45(225.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e126.67(356.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e48.65(124.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBicarbonate, m Eq/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e24.19(4.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22.51(5.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24.92(3.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal bilirubin, mg/dL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.89(1.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.99(1.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.85(1.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChloride, m Eq/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e103.24(5.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e104.02(6.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e102.90(4.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCreatinine, mg/dL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.23(0.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.92(1.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.92(0.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePotassium, m Eq/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.23(0.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.44(0.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.14(0.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSodium, m Eq/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e138.89(4.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e138.95(4.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e138.86(3.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrea nitrogen, mg/dL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e24.18(15.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e36.88(20.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18.61(8.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHematocrit, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e35.22(6.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e33.07(6.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e36.16(6.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemoglobin, g/dL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11.64(2.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10.83(2.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12.00(2.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINR, PT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.39(0.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.54(0.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.33(0.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMCH, pg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e30.17(2.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30.04(2.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30.23(2.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.118\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMCHC, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e33.03(1.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e32.78(1.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e33.14(1.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMCV, fL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e91.42(6.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e91.74(6.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e91.28(6.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.147\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatelet, K/uL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e215.40(99.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e203.59(103.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e220.59(96.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePT, s\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15.12(6.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16.60(7.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14.47(5.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAPTT, s\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e38.49(25.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e40.91(27.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e37.43(23.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRDW, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14.66(1.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15.33(2.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14.37(1.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRBC, m/uL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.87(0.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.63(0.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.98(0.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWBC, K/uL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9.94(5.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11.42(6.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9.30(4.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGlucose, mg/dL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e127.69(52.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e141.30(62.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e121.71(45.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMortality, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e28-day mortality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e209(10.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e118(19.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e91(6.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1-year mortality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e556(28.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e268(44.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e288(21.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eAbbreviations\u003c/b\u003e: AF, atrial fibrillation; AKI, acute kidney injury; ALT, alanine aminotransferase; AST, aspartate aminotransferase; CCB, calcium channel blockers CHF, congestive heart failure; CHD, coronary heart disease; COPD, chronic obstructive pulmonary disease; HTN, hypertension; MBP, mean blood pressure; INR, international normalized ratio; MCH, mean corpuscular hemoglobin; MCHC, mean corpuscular hemoglobin concentration; MCV, mean corpuscular volume; PT, prothrombin time; APTT, activated partial thromboplastin time; RBC: red blood cells; RDW, red blood cell distribution width; WBC: white blood cells.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSurvival Analysis According to the CAR\u003c/h3\u003e\n\u003cp\u003eKM analysis revealed significantly higher mortality rates in the high-CAR group than in the low-CAR group across all time points (log-rank \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for all). Specifically, the 28-day mortality rate was 19.6% in the high CAR group versus 6.6% in the low CAR group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), and the 1-year mortality rates were 44.6% and 21.0%, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). The results revealed that patients with high CAR characteristics had poorer prognoses than those with low CAR characteristics, both in terms of short-term and long-term survival.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eAssociations between the CAR and mortality risk\u003c/h3\u003e\n\u003cp\u003eCox proportional hazards models demonstrated that the CAR was independently associated with both short- and long-term mortality after adjusting for confounders. When CAR was used as a continuous variable, for 28-day mortality, the hazard ratio (HR) and 95% confidence interval (CI) were reported as 2.454 (2.04\u0026ndash;2.95), 2.679 (2.17\u0026ndash;3.3), and 1.873 (1.39\u0026ndash;2.53) in Model 0 (unadjusted), Model 1 (adjusted for age, gender, and race), and Model 2 (further adjusted for age, gender, race, white blood cell, red blood cell distribution width, vasoactive drug, conservation therapy, total bilirubin, aneurysm rupture, AKI, congestive heart failure, coronary heart disease, chronic obstructive pulmonary, atrial fibrillation, conservation therapy, hypertension and glucose), respectively (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and for 360-day mortality, the HR and 95% CI were 2.21 (1.94\u0026ndash;2.51) in Model 0, 2.388 (2.06\u0026ndash;2.8) in Model 1, and 1.955 (1.61\u0026ndash;2.37) in Model 2 (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). When CAR was used as a binary variable, the results revealed that the 28-day mortality for HR and 95% CI were 3.19 (2.40\u0026ndash;4.20), 2.816 (2.13\u0026ndash;3.7), and 1.668 (1.22\u0026ndash;2.29), respectively (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and the 1-year mortality were 2.21 (1.94\u0026ndash;2.51), 2.388 (2.06\u0026ndash;2.8), and 1.955 (1.61\u0026ndash;2.37), respectively (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) in Model 0, Model 1 and Model 2 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e ).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eUnivariate and multivariate Cox regression analyses of 28day and 1year all-cause mortality association with CAR in patients with AA.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModel 0\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e28day all-cause mortality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHR(95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHR(95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHR(95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCAR\u0026thinsp;\u0026ge;\u0026thinsp;0.391\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.19 (2.40\u0026ndash;4.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.82 (2.13\u0026ndash;3.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.40 (1.01\u0026ndash;1.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.043\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCAR\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.45 (2.04\u0026ndash;2.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.68 (2.17\u0026ndash;3.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.53 (1.09\u0026ndash;2.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1year all-cause mortality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHR(95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHR(95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHR(95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCAR\u0026thinsp;\u0026ge;\u0026thinsp;0.391 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.58 (2.20\u0026ndash;3.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.23 (1.88\u0026ndash;2.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.52 (1.24\u0026ndash;1.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCAR\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.21 (1.94\u0026ndash;2.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.39 (2.06\u0026ndash;2.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.72 (1.39\u0026ndash;2.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eModel 0: unadjusted;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eModel 1: adjusted for age, gender, and race;\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eModel 2: adjusted for Age, gender, race, white blood cell, red blood cell distribution width, vasoactive drug, conservation therapy, total bilirubin, aneurysm rupture, AKI, congestive heart failure, coronary heart disease, chronic obstructive pulmonary, atrial fibrillation, conservation therapy, hypertension and glucose. \u003csup\u003ea\u003c/sup\u003e, CAR as a binary variable; \u003csup\u003eb\u003c/sup\u003e, CAR as a continuous variable.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eSensitivity analysis of CAR on mortality association over multiple time points\u003c/h2\u003e\u003cp\u003eTo further evaluate the robustness of the association between the CAR and patient mortality, sensitivity analyses were conducted using additional clinical endpoints. Cox proportional hazards regression models were applied to assess the association of CAR across 3-month and 6-month all-cause mortality. The same covariates used in the primary multivariate models were included in the adjustment to ensure consistency across time points. In the sensitivity analyses, When CAR was used as a continuous variable, CAR remained significantly associated with increased risk of mortality at all alternative time points, HR(95%CI)of 3-month mortality 1.64 (1.27\u0026ndash;2.11), and 6-month mortality 1.67 (1.32\u0026ndash;2.10), when CAR was used as a binary variable, higher CAR remained significantly associated with increased risk of mortality at 90-day 1.60 (1.25\u0026ndash;2.05) and 180 day 1.59 (1.27\u0026ndash;1.99). The trend was consistent with the results observed in the 28-day and 1-year mortality models, supporting the robustness and temporal stability of the association between CAR and adverse outcomes in patients with aortic aneurysms. Further details of these sensitivity analyses, including model coefficients and confidence intervals, are provided in (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eUnivariate and multivariate Cox regression analyses of 90-day and 180-day all-cause mortality association with CAR of AA patients\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModel 0\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e180day all-cause mortality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHR(95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHR(95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHR(95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCAR\u0026thinsp;\u0026ge;\u0026thinsp;0.391\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.00\u003c/p\u003e\u003cp\u003e(2.50\u0026ndash;3.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.69 (2.17\u0026ndash;3.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.60\u003c/p\u003e\u003cp\u003e(1.24\u0026ndash;2.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCAR\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.33\u003c/p\u003e\u003cp\u003e(2.00\u0026ndash;2.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.51 (2.11\u0026ndash;2.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.64\u003c/p\u003e\u003cp\u003e(1.27\u0026ndash;2.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e180day all-cause mortality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHR(95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHR(95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHR(95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCAR\u0026thinsp;\u0026ge;\u0026thinsp;0.391\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.91\u003c/p\u003e\u003cp\u003e(2.40\u0026ndash; 3.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.55 (2.10\u0026ndash;3.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.59\u003c/p\u003e\u003cp\u003e(1.27\u0026ndash;1.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCAR\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.28\u003c/p\u003e\u003cp\u003e(1.98\u0026ndash;2.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.47 (2.10\u0026ndash;2.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.67\u003c/p\u003e\u003cp\u003e(1.32\u0026ndash;2.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eModel 0: unadjusted;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eModel 1: adjusted for age, gender, and race;\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eModel 2: adjusted for Age, gender, race, white blood cell, red blood cell distribution width, vasoactive drug, conservation therapy, total bilirubin, aneurysm rupture, AKI, congestive heart failure, coronary heart disease, chronic obstructive pulmonary, atrial fibrillation, conservation therapy, hypertension and glucose. \u003csup\u003ea\u003c/sup\u003e, CAR as a binary variable; \u003csup\u003eb\u003c/sup\u003e, CAR as a continuous variable.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eEvaluation of the nonlinear association between the CAR and all-cause mortality\u003c/h2\u003e\u003cp\u003eWe used RCS analysis to assess whether a linear relationship exists between CAR and 28-day and 1-year all-cause mortality. The adjusted RCS curve revealed a significant association between the CAR and short- and long-term all-cause mortality (\u003cem\u003eP\u003c/em\u003e for overall\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas a significant nonlinear effect was observed (\u003cem\u003eP\u003c/em\u003e for nonlinearity\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This suggested that the CAR was associated with 28-day and 1-year mortality, with a key threshold of 0.391, above which the risk of mortality increased. For short-term mortality, high CAR levels were significantly associated with an increased risk of mortality (HR\u0026thinsp;=\u0026thinsp;3.0, 95% CI: 2\u0026ndash;4.19, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas low CAR levels were significantly associated with a decreased risk of mortality (HR\u0026thinsp;=\u0026thinsp;0.3, 95% CI: 0.2\u0026ndash;0.412, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Similarly, with respect to long-term mortality, comparable results were observed (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The results revealed the nonlinear relationship.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eExploratory subgroup analysis of the CAR and mortality\u003c/h2\u003e\u003cp\u003eTo investigate the association between CAR and all-cause mortality in patients with AA, exploratory subgroup analyses were performed by stratifying the population according to gender, race, age\u0026thinsp;\u0026ge;\u0026thinsp;65 years, aneurysm rupture status, and aneurysm sites. Overall, elevated CAR was significantly associated with increased risk of both 28-day and 1-year all-cause mortality across nearly all subgroups. For 28-day mortality, the (HR -2.45, 95% CI: 2.04\u0026ndash;2.95; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Similarly, for 1-year mortality, the (HR 2.21, 95% CI: 1.94\u0026ndash;2.51; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). At 28 days, A significant interaction was observed in the analysis of 28-day mortality (P for interaction\u0026thinsp;=\u0026thinsp;0.034). The association between elevated CAR and short-term mortality was stronger among other races (HR: 3.36, 95% CI: 2.42\u0026ndash;4.66) compared to White patients (HR: 2.18, 95% CI: 1.68\u0026ndash;2.82). Additionally, a significant interaction was also noted for 1-year mortality (P for interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating that the association between elevated CAR and long-term mortality was more pronounced in other race patients (HR: 2.03, 95% CI: 2.73\u0026ndash;4.07) compared to White patients (HR: 3.10, 95% CI: 1.37\u0026ndash;2.38). Significant interactions were observed for race, age\u0026thinsp;\u0026ge;\u0026thinsp;65 years, and aneurysm rupture in the association between CAR and 1-year all-cause mortality (all P for interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Notably, the interaction effects of age and aneurysm rupture on CAR were only evident in 1-year mortality, but not in short-term outcomes. Among patients aged\u0026thinsp;\u0026lt;\u0026thinsp;65 years, elevated CAR was associated with an increased risk of death (HR\u0026thinsp;=\u0026thinsp;2.10, 95% CI: 1.63\u0026ndash;2.72, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while the association was even stronger in those aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years (HR\u0026thinsp;=\u0026thinsp;3.00, 95% CI: 2.43\u0026ndash;3.69, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; P for interaction\u0026thinsp;=\u0026thinsp;0.036). Similarly, the effect of CAR was more pronounced in patients with ruptured aneurysms (HR\u0026thinsp;=\u0026thinsp;2.98, 95% CI: 1.75\u0026ndash;5.16, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to those without rupture (HR\u0026thinsp;=\u0026thinsp;2.14, 95% CI: 1.86\u0026ndash;2.46, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; P for interaction\u0026thinsp;=\u0026thinsp;0.031).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we found that elevated CAR levels were significantly associated with increased all-cause mortality in patients with AA, both in the short term (28-day mortality) and long term (1-year mortality). Notably, both short-term and long-term mortality rates were significantly higher among patients with CAR ≥ 0.391 compared to those with CAR \u0026lt; 0.391, highlighting the prognostic value of CAR in this patient population. Restricted cubic spline analysis suggests a potentially nonlinear association, suggesting that even modest increases in CAR may herald disproportionately higher risk beyond the inflection point, highlighting the complexity of the role of CAR in predicting the mortality of AA patients. This difference may be attributed to variations in patient demographics and the inclusion of diverse aneurysm types in our cohort.\u003c/p\u003e\n\u003cp\u003eMechanistically, elevated creatinine reflects not only impaired renal clearance but also muscle wasting and microvascular dysfunction—processes that contribute to aneurysm growth and weakening of the aortic wall. Hypoalbuminemia, on the other hand, indicates poor nutritional reserves and heightened inflammatory activity, both of which accelerate extracellular matrix degradation in the aortic wall. The creatinine-to-albumin ratio (CAR) integrates these pathophysiological pathways, providing a more comprehensive assessment of patient vulnerability than either marker alone.\u003c/p\u003e\n\u003cp\u003eOur findings align with previous studies that have demonstrated the prognostic utility of CAR across various clinical conditions, including acute kidney injury (AKI) and cardiovascular diseases[22]. For instance, CAR has been identified as an independent predictor of mortality in patients with chronic heart failure, reflecting the interplay between renal dysfunction, systemic inflammation, and nutritional depletion key determinants of prognosis in this population. Similarly, in patients with acute coronary syndrome (ACS) undergoing percutaneous coronary intervention (PCI), CAR has been shown to outperform individual biomarkers such as creatinine or albumin in risk stratification, highlighting its value as a composite marker that integrates multiple pathophysiological pathways[23, 24]. Our study extends these findings by demonstrating that CAR is a robust predictor of mortality in patients with aortic aneurysm (AA), underscoring its broader applicability in vascular disease contexts.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eThe strong association between CAR and mortality in AA patients may reflect underlying renal function, nutritional status and inflammatory. Well established markers of renal function, such as elevated creatinine, may cause the poor clinical outcomes such as heart failure[25], acute coronary syndrome[26], have been identified, and high creatinine could be highly dangerous factor; for example, aneurysm rupture is variable in predicting the prognosis of AA patients, and higher rates of postoperative morbidity and mortality[27]. This aligns with prior evidence showing that impaired renal function, manifested by elevated creatinine, contributes to systemic inflammation and metabolic disturbances, which are critical drivers of adverse outcomes in vascular diseases. The most abundant protein in human plasma and primarily synthesized by hepatocytes, plays a critical role in maintaining colloid osmotic pressure, transporting various endogenous and exogenous substances, and modulating inflammatory and oxidative stress responses. Low serum albumin levels are widely recognized as a marker of systemic illness severity and poor nutritional status, and have been consistently associated with adverse clinical outcomes across a broad spectrum of diseases[28–36], including cardiovascular diseases, respiratory diseases, digestive diseases, tumor diseases, urinary diseases and so on, some of those diseases may to be the acceleration of development about the AA. In particular, hypoalbuminemia has been implicated in the pathogenesis of abdominal aortic aneurysm (AAA), potentially through mechanisms involving impaired antioxidant defense and increased vascular wall inflammation. Experimental and clinical evidence suggests that albumin exerts protective effects by promoting the synthesis of anti-inflammatory mediators, such as lipoxins and other bioactive molecules during periods of oxidative stress, which may help mitigate vascular damage and reduce AAA expansion risk [37, 38]. Moreover low preoperative serum albumin levels have been independently associated with increased postoperative morbidity and mortality following endovascular aneurysm repair (EVAR)[39]. This association underscores the importance of nutritional status in surgical outcomes and supports the potential value of preoperative nutritional optimization. Indeed, studies have demonstrated that albumin supplementation or targeted nutritional support can improve clinical outcomes in patients with severe hypoalbuminemia and poor baseline health status[40].\u003c/p\u003e\n\u003cp\u003eClinically, the CAR offers a highly convenient, rapid, and economical means of risk stratification in aortic aneurysm care. Because it relies solely on serum creatinine and albumin—tests already performed on virtually every patient at admission CAR incurs no additional cost or workload, can be calculated at the bedside in seconds, and is immediately actionable. Its universal availability makes it suitable for high-tech centers and resource-limited settings alike. By pinpointing high-risk individuals early, CAR facilitates targeted surveillance, timely intervention, and more efficient allocation of healthcare resources, thereby enhancing patient safety and optimizing treatment outcomes. Our analysis revealed a consistent and statistically significant association between elevated CAR levels and increased all-cause mortality across multiple time points, ranging from the acute phase (28-day mortality) to longer-term follow-up (1-year mortality). Notably, sensitivity analyses further validated this association at intermediate time intervals, reinforcing the stability and reliability of CAR as a prognostic marker throughout the clinical course of AA. Additionally, subgroup analyses also highlighted variations in the predictive performance of CAR across different patient populations. For instance, the prognostic value of CAR was particularly pronounced among male patients and older adults (\u0026gt; 65 years), those with aortic aneurysm[41], suggesting that age and sex may modulate the relationship between functional status of renal, nutritional status, and mortality in AA. These findings align with existing literature indicating that older individuals and males are at higher risk for adverse outcomes following vascular events. Furthermore, we observed that certain racial subgroups, such as other populations including Black and Asian patients, tended to present with larger aneurysm diameters[42]. This finding may reflect disparities in disease detection, access to care, or underlying biological differences, which could contribute to greater aneurysm complexity and increased technical challenges during interventions such as EVAR. The interplay between race, aneurysm morphology, and CAR levels warrants further investigation to better understand how these factors influence clinical decision-making and outcomes. Therefore, these insights support the application of CAR within a personalized medicine framework, where individual patient characteristics such as age, gender, race, and baseline comorbidities can inform tailored risk assessment and management strategies. Importantly, the predictive power of CAR spans both the immediate post-diagnosis period and long-term follow-up, offering clinicians a unified and dynamic biomarker for continuous risk monitoring throughout the disease trajectory of aortic aneurysm.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eThere are several limitations that warrant consideration. Firstly, the retrospective nature of our analysis may introduce inherent biases, including missing data and unmeasured confounding variables. Additionally, certain important clinical parameters, such as aneurysm diameter, were not available and could not be adjusted for in the analysis. Secondly, although our cohort included patients from multiple racial backgrounds, the majority were White, and all data were derived from a single center in the United States. These factors may limit the generalizability of our findings. Therefore, future multicenter studies with more ethnically and geographically diverse populations are needed to externally validate our results. Finally, although CAR reflects the interplay between renal function and nutritional status, it remains unclear whether therapeutic strategies targeting these underlying components can influence CAR levels or improve outcomes in AA patients. Future mechanistic and interventional studies are warranted to explore this potential.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn patients with aortic aneurysm, the serum creatinine-to-albumin ratio independently predicts both short-term and long-term all-cause mortality. The CAR cutoff of 0.391 provides a simple, low-cost, and widely accessible metric for early identification of high-risk patients, supporting personalized surveillance and treatment strategies that can improve clinical care across diverse practice settings.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eAA\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAortic aneurysm\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eAAA\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAbdominal aortic aneurysm\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eACS\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAcute coronary syndrome\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eAF\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAtrial fibrillation\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eAKI\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAcute kidney injury\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eALT\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAlanine aminotransferase\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eAST\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAspartate aminotransferase\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eAPTT\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eActivated partial thromboplastin time\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eCAR\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCreatinine-to-albumin ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eCCB\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCalcium channel blockers\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eCHF\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCongestive heart failure\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eCHF\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCongestive heart failure\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eCHD\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCoronary heart disease\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eCOPD\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eChronic obstructive pulmonary disease\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eCTA\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eComputed Tomography Angiography\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eEVAR\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eEndovascular aneurysm repair\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eHR\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHazard ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eINR\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eInternational normalized ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eICU\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eIntensive care unit (ICU)\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eKM\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eKaplan-Meier survival analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eMCH\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMean corpuscular hemoglobin\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eMCHC\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMean corpuscular hemoglobin concentration\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eMCV\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMean corpuscular volume\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eMIMIC\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMedical Information Mart for Intensive Care\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eNLR\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cb\u003eN\u003c/b\u003eeutrophil-to-lymphocyte ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eRCS\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eRestricted cubic spline\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eRDW\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eRed blood cell distribution width\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eRBC\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eRed blood cells\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003ePCI\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePercutaneous coronary intervention\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003ePT\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eProthrombin time\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eWBC\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eWhite blood cells\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e95% confidence interval\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe following authors contributed to the preparation of the manuscript as follows: DC: Data gathering, Study design, Statistical analysis, manuscript editing, interpretation of data, literature search. JF, JW: Statistical analysis, manuscript editing, interpretation of data, CG, CX, ZW, MY: Data gathering, Study design. AH, GL, LS: Study design. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cpre\u003eFinancial backing for the research, writing, and publication of this article was recognized by the author(s), include National Natural Science Foundation of China [Grant no. 82200519 and 8220020445], and the Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China, [Grant no. Y0120220151], Outstanding Young Scientists of Tongji Hospital, Tongji University [Grant no. HBRC 1801] and the Tongji Hospital Internal Training Program [Grant no. ITJ-QN2404], National Natural Science Foundation of China [Grant Numbers: 82400345], 2025 Basic and Applied Basic Research Special Topic: Young Doctoral “Starting Sail” Project [2025A04J4716].\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval of studies and informed consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe use of public databases does not require ethical approval or informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to extend our sincere gratitude to MIMIC-III and MIMIC-IV participants and staff for their invaluable support and assistance. We would like to thank the funders and all original authors who provided publicly available data. ChatGPT (OpenAI, San Francisco, USA) was used to improve the grammar and clarity of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe corresponding author can be contacted to receive the datasets generated and utilized in this work upon reasonable request and with MIMIC’s permission.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMarcaccio CL, Schermerhorn ML. Epidemiology of abdominal aortic aneurysms. Semin Vasc Surg. 2021;34(1):29\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNordon IM, et al. Pathophysiology and epidemiology of abdominal aortic aneurysms. Nat Rev Cardiol. 2011;8(2):92\u0026ndash;102.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchanzer A, Oderich GS. Management of Abdominal Aortic Aneurysms. 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Heart. 2019;105(24):1876\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNam WS, et al. Prognostic Value of Serum Albumin in Aortic Aneurysm Patients Undergoing Graft Replacement of Ascending Aorta and Aortic Arch. Int J Med Sci. 2023;20(5):663\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBjorck M. Management of the tense abdomen or difficult abdominal closure after operation for ruptured abdominal aortic aneurysms. Semin Vasc Surg. 2012;25(1):35\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eObel LM, et al. Population-Based Risk Factors for Ascending, Arch, Descending, and Abdominal Aortic Dilations for 60-74-Year-Old Individuals. J Am Coll Cardiol. 2021;78(3):201\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ede Guerre L, et al. Racial Differences in Isolated Aortic, Concomitant Aortoiliac, and Isolated Iliac Aneurysms: This is a Retrospective Observational Study. Ann Surg. 2023;277(1):165\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAdditional. file 1.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"creatinine-to-albumin ratio, aortic aneurysm, mortality, prognosis, rupture","lastPublishedDoi":"10.21203/rs.3.rs-6935801/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6935801/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eAortic aneurysm is a life-threatening vascular disorder marked by progressive aortic dilation. Early stages are often clinically silent, and diagnosis depends predominantly on incidental imaging, which limits opportunities for timely treatment. Conventional inflammatory and thrombotic biomarkers demonstrate modest specificity and are vulnerable to systemic confounders. The serum creatinine-to-albumin ratio-a composite indicator of inflammation, metabolic stress, and nutritional status-has proven prognostic relevance in other cardiovascular settings but remains unexamined in aortic aneurysm.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003e We conducted a retrospective cohort study using adult patient data from the MIMIC-III and MIMIC-IV intensive care databases. Baseline serum creatinine and albumin measurements defined the ratio, and an optimal cutoff (0.391) was derived by maximally selected rank statistic. Patients were stratified into high- and low-ratio groups. Kaplan-Meier analysis compared 28-day and one-year survival probabilities, while multivariable Cox proportional hazards models quantified the ratio\u0026rsquo;s association with mortality, adjusting for demographic and clinical covariates. Restricted cubic spline regression assessed nonlinear risk relationships, and sensitivity analyses at 90 and 180 days verified temporal consistency. Subgroup analyses evaluated effect modification by age, aneurysm rupture, and other key factors.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAmong 1,970 patients, a CAR of \u0026ge;\u0026thinsp;0.391 was associated with significantly worse survival. Multivariate Cox regression revealed higher CAR levels were linked to increased mortality risk: 28-day mortality (hazard ratio (HR) 1.53; 95% confidence interval (95%CI) 1.09\u0026ndash;2.14) and 1-year mortality (HR 1.51; 95% CI 1.24\u0026ndash;1.85). Kaplan-Meier analysis showed reduced survival rates in high CAR patients at all time points (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Sensitivity analyses confirmed consistent associations with 90-day and 180-day mortality (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Restricted cubic spline analysis demonstrated a nonlinear increase in mortality risk with rising CAR values. Subgroup analyses identified older patients and those with ruptured aneurysms as particularly vulnerable.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThe serum creatinine-to-albumin ratio is a simple, low-cost prognostic biomarker in aortic aneurysm. A cutoff of 0.391 reliably identifies individuals at elevated short-term and long-term mortality risk, supporting its use in early risk stratification and personalized management.\u003c/p\u003e","manuscriptTitle":"Prognostic Value of the Serum Creatinine-to-Albumin Ratio for Short-term and Long-term Mortality Among Patients with Aortic Aneurysm: A Retrospective Cohort Study Running title: Creatinine-to-albumin ratio predicts mortality in patients with aortic aneurysms","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-19 13:39:37","doi":"10.21203/rs.3.rs-6935801/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d9819af3-3915-4637-8990-918dc1e60fbf","owner":[],"postedDate":"August 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-04T10:57:20+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-19 13:39:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6935801","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6935801","identity":"rs-6935801","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}